A typical convolutional neural network for image segmentation usually consists of a convolutional feature extractor whose topology is similar to common classification networks to extract features from input images automatically and following convolution-based operations to generate final dense predictions. In the field of natural image segmentation, FCN [1]}, DeepLab [2]}, PSPNet [3]} and SegNet [4]} are rather popular for their performance and efficiency. For medical segmentation, U-Net [5]} has been rather popular in many tasks, and has been modified into many improved versions such as Attention U-Net [6]}, U-Net++ [7]}, V-Net [8]} and H-DenseUNet [9]} to acquire better performance in certain fields.
